@article {75,
	title = {Computerized adaptive testing using the nearest-neighbors criterion},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {3},
	year = {2003},
	pages = {204-216},
	abstract = {Item selection procedures designed for computerized adaptive testing need to accurately estimate every taker{\textquoteright}s trait level (θ) and, at the same time, effectively use all items in a bank. Empirical studies showed that classical item selection procedures based on maximizing Fisher or other related information yielded highly varied item exposure rates; with these procedures, some items were frequently used whereas others were rarely selected. In the literature, methods have been proposed for controlling exposure rates; they tend to affect the accuracy in θ estimates, however. A modified version of the maximum Fisher information (MFI) criterion, coined the nearest neighbors (NN) criterion, is proposed in this study. The NN procedure improves to a moderate extent the undesirable item exposure rates associated with the MFI criterion and keeps sufficient precision in estimates. The NN criterion will be compared with a few other existing methods in an empirical study using the mean squared errors in θ estimates and plots of item exposure rates associated with different distributions. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Item Analysis, Item Response Theory, Statistical Analysis, Statistical Estimation computerized adaptive testing, Statistical Tests},
	author = {Cheng, P. E. and Liou, M.}
}
@article {74,
	title = {Estimation of trait level in computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {24},
	number = {3},
	year = {2000},
	pages = {257-265},
	abstract = {Notes that in computerized adaptive testing (CAT), a examinee{\textquoteright}s trait level (θ) must be estimated with reasonable accuracy based on a small number of item responses. A successful implementation of CAT depends on (1) the accuracy of statistical methods used for estimating θ and (2) the efficiency of the item-selection criterion. Methods of estimating θ suitable for CAT are reviewed, and the differences between Fisher and Kullback-Leibler information criteria for selecting items are discussed. The accuracy of different CAT algorithms was examined in an empirical study. The results show that correcting θ estimates for bias was necessary at earlier stages of CAT, but most CAT algorithms performed equally well for tests of 10 or more items. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {(Statistical), Adaptive Testing, Computer Assisted Testing, Item Analysis, Statistical Estimation computerized adaptive testing},
	author = {Cheng, P. E. and Liou, M.}
}
